2.4. Analysis of Literature

Individual scenarios are considered independent entities in the database. Clearly,
in practice, individual scenarios are often related to each other and are not
always developed independently. Some are simply variants of others generated
for a particular purpose. Many "new" scenarios are designed to track existing
benchmark scenarios. A good example is the set of IS92 scenarios, especially
the "central" IS92a scenario, which was often used as a reference from which
to develop other scenarios. A further consideration is that not all scenarios
are created in an equal fashion. Some are the result of elaborate effort, which
includes extensive reviews and revisions; others are simply the outcome of input
assumptions without much significant reflection. Some are based on extensive
formal models, while others are generated using simple spreadsheets or even
without any formal tools at all.

Numerous factors influence future emissions paths in the scenarios. Clearly,
demographic and economic developments play a crucial role in determining emissions.
However, many other factors are involved also, from human resources, such as
education, institutional frameworks, and lifestyles, to natural resource endowments,
technologic change, and international trade. Many of these important factors
are not documented in the database, and sometimes not even in the respective
scenario reports and publications. Some are neither quantified in the scenarios
nor explicitly assumed in a narrative form.

For this analysis, a simple scheme is used to decompose the main driving forces
of GHG emissions. This scheme is based on the Kaya identity (Kaya, 1990; Yamaji
et al., 1991), which gives the main emissions driving forces as multiplicative
factors on one side of the identity and total CO2 (or GHG) emissions on the
other. It multiplies population growth, per capita value added (i.e., per capita
gross world product), energy consumption per unit value added, and emissions
per unit energy on one side of the identity, and total CO2 emissions on the
other side (Yamaji et al., 1991)3;
it is a specific application of a frequently used approach to organize discussion
of the drivers of emissions through the so-called IPAT identity that relates
impacts (I) to population (P) multiplied by affluence (A) and technology (T),
(see Chapter 3 for a more detailed discussion). The same
approach can be used for other emissions such as SO2. However, the driving
forces might be different for some species of anthropogenic emissions.

Apart from its simplicity, an advantage of analysis that uses the Kaya identity
to decompose emissions into four main driving forces is that it facilitates
at least some standardization in the comparison and analysis of many diverse
emissions scenarios. This decomposition is very useful because it indicates
where to seek differences in scenario assumptions that may account for differences
in the resultant GHG emissions (Alcamo et al., 1995). However, the identity
is not used here to suggest causality. An important caveat is that these driving
forces are not independent of each other; in many scenarios they explicitly
depend on each other. For example, scenario builders often assume that high
rates of economic growth lead to high capital turnover. This favors more advanced
and more efficient technologies, which result in lower energy intensities. Sometimes
a weak inverse relationship is assumed between population and economic growth.
Thus, the scenario ranges for these main driving forces are not (necessarily)
independent of each other. (See also the discussion of the relationships between
the main scenario driving forces in Chapter 3.)

In the following sections, scenario ranges are presented for each of the four
factors in the Kaya identity that represent the main (energy-related) emissions
driving forces: population, gross world product, energy consumption, energy
intensity (energy per unit of gross world product) and carbon intensity (CO2
emissions per unit of energy). The ranges for CO2 and SO2 emissions are presented
first because they represent the "dependent variable" in the Kaya identity.
These are followed by scenario ranges for the other factors in the decomposition
that represent the "independent variables" (main emissions scenario driving
forces) in the identity. This sequence was chosen to present the main scenario
driving forces because it corresponds to their representation in the Kaya identity;
it does not imply a priori any causal relationships among the driving forces
themselves or between the driving forces and CO2 emissions.

Four complementary methods of analysis are used:

charts that show the distributions of scenarios in terms of their main characteristics
and driving forces, including CO2 emissions, population growth, global GDP,
energy consumption, energy intensity, and carbon intensity;

histograms that show the range of values of main scenario driving forces,
together with associated statistics such as the mean, minimum, and maximum
values;

"snowflake" diagrams, in which each of the axes represents the range of
one of the key driving forces; and

analysis of the relationships among the main driving forces of energy-related
CO2 emissions.

The main findings of this scenario analysis are reported in Nakicenovic et
al. (1998a).